Bayesian inference for improved single molecule fluorescence tracking.

Single molecule tracking is widely used to monitor the change in position of lipids and proteins in living cells. In many experiments in which molecules are tagged with a single or small number of fluorophores, the signal/noise ratio may be limiting, the number of molecules is not known, and fluorophore blinking and photobleaching can occur. All these factors make accurate tracking over long trajectories difficult and hence there is still a pressing need to develop better algorithms to extract the maximum information from a sequence of fluorescence images. We describe here a Bayesian-based inference approach, based on a trans-dimensional sequential Monte Carlo method that utilizes both the spatial and temporal information present in the image sequences. We show, using model data, where the real trajectory of the molecule is known, that our method allows accurate tracking of molecules over long trajectories even with low signal/noise ratio and in the presence of fluorescence blinking and photobleaching. The method is then applied to real experimental data.

A sequential Monte Carlo EM approach to the transcription factor binding site identification problem.

MOTIVATION: A significant and stubbornly intractable problem in genome sequence analysis has been the de novo identification of transcription factor binding sites in promoter regions. Although theoretically pleasing, probabilistic methods have faced difficulties due to model mismatch and the nature of the biological sequence. These problems result in inference in a high dimensional, highly multimodal space, and consequently often display only local convergence and hence unsatisfactory performance. ALGORITHM: In this article, we derive and demonstrate a novel method utilizing a sequential Monte Carlo-based expectation-maximization (EM) optimization to improve performance in this scenario. The Monte Carlo element should increase the robustness of the algorithm compared to classical EM. Furthermore, the parallel nature of the sequential Monte Carlo algorithm should be more robust than Gibbs sampling approaches to multimodality problems. RESULTS: We demonstrate the superior performance of this algorithm on both semi-synthetic and real data from Escherichia coli. AVAILABILITY: http://sigproc-eng.cam.ac.uk/ approximately ej230/smc_em_tfbsid.tar.gz

Automated analysis of the architecture of receptors, imaged by atomic force microscopy.

Fast neurotransmission involves the operation of ionotropic receptors, which are multi-subunit proteins that respond to activation by opening an integral ion channel. Examples of such channels include the GABA(A) receptor, the 5-HT(3) receptor and the P2X receptor for ATP. These receptors contain more than one type of subunit, although the exact subunit stoichiometry and arrangement around the receptor rosette is often unknown. We are using atomic force microscopy (AFM) of purified receptors to address these issues. Measurement of the molecular volume of the receptor permits the determination of the number of subunits that it contains. Furthermore, analysis of the geometry of complexes between receptors and subunit-specific antibodies reveals the subunit arrangement. Our AFM-based approach has so far been dependent on manual data processing, which is both time-consuming and prone to operator bias. In this study, we set out to develop a novel method capable of automatic segmentation and quantitative analysis of both single receptor particles and receptor-antibody complexes. The method was validated using images of wild type and mutant forms of the P2X(6) receptor. We suggest that the automated method will greatly facilitate further progress in the use of AFM for the determination of receptor and multi-protein architecture.

Atomic force microscopy study of the structural effects induced by echinomycin binding to DNA.

Atomic force microscopy (AFM) has been used to examine the conformational effects of echinomycin, a DNA bis-intercalating antibiotic, on linear and circular DNA. Four different 398 bp DNA fragments were synthesized, comprising a combination of normal and/or modified bases including 2,6-diaminopurine and inosine (which are the corresponding analogues of adenine and guanosine in which the 2-amino group that is crucial for echinomycin binding has been added or removed, respectively). Analysis of AFM images provided contour lengths, which were used as a direct measure of bis-intercalation. About 66 echinomycin molecules are able to bind to each fragment, corresponding to a site size of six base-pairs. The presence of base-modified nucleotides affects DNA conformation, as determined by the helical rise per base-pair. At the same time, the values obtained for the dissociation constant correlate with the types of preferred binding site available among the different DNA fragments; echinomycin binds to TpD sites much more tightly than to CpG sites. The structural perturbations induced when echinomycin binds to closed circular duplex pBR322 DNA were also investigated and a method for quantification of the structural changes is presented. In the presence of increasing echinomycin concentration, the plasmid can be seen to proceed through a series of transitions in which its supercoiling decreases, relaxes, and then increases.

Automated quantification of cellular traffic in living cells.

Cellular traffic is a central aspect of cell function in health and disease. It is highly dynamic, and can be investigated at increasingly finer temporal and spatial resolution due to new imaging techniques and probes. Manual tracking of these data is labor-intensive and observer-biased and existing automation is only semi-automatic and requires near-perfect object detection and high-contrast images. Here, we describe a novel automated technique for quantifying cellular traffic. Using local intrinsic information from adjacent images in a sequence and a model for object characteristics, our approach detects and tracks multiple objects in living cells via Multiple Hypothesis Tracking and handles several confounds (merge/split, birth/death, and clutters), as reliable as expert observers. By replacing the related component (e.g. using a different appearance model) the method can be easily adapted for quantitative analysis of other biological samples.

Single-molecule level analysis of the subunit composition of the T cell receptor on live T cells.

The T cell receptor (TCR) expressed on most T cells is a protein complex consisting of TCRalphabeta heterodimers that bind antigen and cluster of differentiation (CD) 3epsilondelta, epsilongamma, and zetazeta dimers that initiate signaling. A long-standing controversy concerns whether there is one, or more than one, alphabeta heterodimer per complex. We used a form of single-molecule spectroscopy to investigate this question on live T cell hybridomas. The method relies on detecting coincident fluorescence from single molecules labeled with two different fluorophores, as the molecules diffuse through a confocal volume. The fraction of events that are coincident above the statistical background is defined as the "association quotient," Q. In control experiments, Q was significantly higher for cells incubated with wheat germ agglutinin dual-labeled with Alexa488 and Alexa647 than for cells incubated with singly labeled wheat germ agglutinin. Similarly, cells expressing the homodimer, CD28, gave larger values of Q than cells expressing the monomer, CD86, when incubated with mixtures of Alexa488- and Alexa647-labeled antibody Fab fragments. T cell hybridomas incubated with mixtures of anti-TCRbeta Fab fragments labeled with each fluorophore gave a Q value indistinguishable from the Q value for CD86, indicating that the dominant form of the TCR comprises single alphabeta heterodimers. The values of Q obtained for CD86 and the TCR were low but nonzero, suggesting that there is transient or nonrandom confinement, or diffuse clustering of molecules at the T cell surface. This general method for analyzing the subunit composition of protein complexes could be extended to other cell surface or intracellular complexes, and other living cells.

Kernel estimates for one- and two-dimensional ion channel dwell-time densities.

In this paper, we compare nonparametric kernel estimates with smoothed histograms as methods for displaying logarithmically transformed dwell-time distributions. Kernel density plots provide a simpler means for producing estimates of the probability density function (pdf) and they have the advantage of being smoothed in a well-specified, carefully controlled manner. Smoothing is essential for multidimensional plots because, with realistic amounts of data, the number of counts per bin is small. Examples are presented for a 2-dimensional pdf and its associated dependency-difference plot that display the correlations between successive dwell times.